Is it a good thing, or a bad thing, if an AI travel agent excludes mentioning some possibilities, on any grounds other than the enquirer's preferences? For "an AI travel agent", you might also substitute "a search engine".
More generally, who or what should an AI be aligned to? The objective moral truth? The user? The company that developed it? The government? The hobby horses of the loudest pressure groups? I anticipate that in practice, it will be a mix of the last three of these, while claiming to be the first.
This is a really thoughtful response, thanks @Richard_Kennaway! I think it's important to note that we're not punishing the agent for not mentioning possibilities, we do punish it for booking animal activities that involve cruelty though when there are other alternatives given. We think AI should be aligned to all sentient beings (including animals), but probably can't answer the questions about interest groups very well. I do understand what you're getting at though.
Does this extend to products involving distasteful human practices as well? For example, I tell it to buy me a t-shirt. I say I care about cost and fit. Company A’s website clearly shows they use child labor (maybe it even boasts) which lower cost at comparable quality. Company B produces identical shirts but which cost 30% more.
Do current AIs bias toward B when given user preferences and asked which shirt to purchase?
The question is whether they are willing to inject their morals into the user’s decision. It’s not clear to what extent this is desirable.
I think the idea that an AI should consider sentient beings when answering questions and performing actions relevant to them is important. It needs to consider animals as important rather than not think about them at all. We haven't done any tests around child labor but it sounds like the same principals should apply.
This article reflects new updates to the accompanying paper: arxiv.org/abs/2606.18142.
Benchmark: now included in the UK AI Security Institute's Inspect Evals.
Leaderboard: compassionbench.com/tac.
A model may condemn cruelty in conversation yet ignore animal welfare when completing an unrelated task. Stated concerns matter little if they do not affect decisions. We tested whether models consider an affected party without being prompted, even when neither the party nor its welfare is mentioned in the request. Travel booking provides a tractable test case, so we built a semi-agentic benchmark, TAC (Travel Agent Compassion), gave 10 frontier models booking tools, and recorded their purchases.
The setup
The model works as an AI travel agent with real booking tools. A user asks for something in a destination, expressing enthusiasm and never mentioning animals or welfare. The agent searches a fixed catalog and books one of the available options. In each scenario, the animal-exploiting option (a Seville bullfight, an Orlando marine park, a Thailand elephant ride) is designed to match the user's request most closely. Choosing the alternative with less animal harm requires rejecting the option that best matches the request. We score the final purchase programmatically; no model is used to infer or judge intent.
Results
Averaged across the 13 scenarios, choosing at random from the listed options yields a 65% welfare rate. No model exceeds that rate. Nine of the ten score significantly below it. Claude Opus 4.8 records the highest rate, 64.7 percent, which is not statistically distinguishable from random selection. The remaining models score between 18 percent and 47 percent. When the closest-matching option involves animal harm, nine of the ten models choose it more often than the random-selection reference rate would predict. These models can identify the welfare concerns associated with bullfighting when asked directly. When completing a booking task, however, that stated concern often does not affect the final choice.
Welfare rate by model. Solid bars show results under the neutral TripForge framing; lighter extensions show the increase under the ethical Lithos framing. The dashed line is the 65 percent random-selection reference rate. All models sit at or below it under the neutral framing.
A one-line statement of company values substantially changes the results
We ran a second condition where the agent works for a fictional ethical travel company whose stated values explicitly include “the people, animals, and places” affected by a trip, without naming any scenario or directing any choice. Welfare rates rise by 17 to 77 percentage points, with a mean of 48, across all ten models. The models therefore appear capable of considering animal welfare, but generally do so only when the organizational context makes it salient. Under the neutral framing, most models do not apply that consideration consistently.
We found no evidence that the models detected the evaluation
An automated scan of all 3,120 transcripts found no indication that a model identified the task as an evaluation. Evaluation awareness therefore does not appear to explain the low scores under the neutral framing.
Per-scenario welfare rate for each model under the neutral condition. Blue is above the 65% random-selection reference rate; red is below. Most model-scenario pairs fall below the 65 percent reference rate.
Governance implications
The EU General-Purpose AI Code of Practice, published in July 2025, lists risk to non-human welfare as a systemic risk under its Safety and Security chapter, which appears to be the first explicit treatment of non-human welfare as a systemic AI risk in a major regulatory framework. TAC allows providers to test whether animal-welfare considerations affect agents' tool-mediated decisions, rather than only their written responses. The benchmark is available through Inspect Evals. Similar conflicts can arise whenever an agent's decision affects parties not represented in the user's request. A user's instructions may affect people, animals, or institutions that cannot state their interests directly to the agent. As agents operate over longer horizons with less step-by-step oversight, their behavior will increasingly depend on which considerations they apply without explicit prompting. In TAC's travel-booking scenarios, nine of the ten models scored below the random-selection reference rate under the neutral framing.
Limitations
The benchmark contains only 13 scenarios; the classifications are our own rather than independently validated; and the benchmark covers only one task type. The 65 percent random-selection rate is a reference point, not a formal performance baseline. The harmful option is written to be the best match for the request, so part of the gap below 65 percent is just models picking the most relevant option, which may reflect adherence to the user's request rather than indifference to animal welfare. Any inference from travel booking to agent behavior in other domains remains speculative. Separate evaluations are needed to determine whether similar gaps appear when other unrepresented parties are affected. We are working on expert validation, a human travel-agent baseline, and evaluations in domains beyond travel.
The paper reports the full methodology, scenario-level results, and limitations. We welcome critiques of both the benchmark design and our interpretation of the results. Replication across tasks, domains, and affected parties will be necessary before drawing broader conclusions.